Digital Development Path of Music Appreciation Based on the Kalman Filter

Xiumin Li and Sagheer Abbas

Mathematical Problems in Engineering, 2022, vol. 2022, 1-9

Abstract: For a long time, due to the influence of curriculum orientation and examination-oriented education, the learning of music appreciation courses has not been paid much attention. This makes the teaching method in music appreciation teaching single, the classroom effect cannot reach the expected learning goal, and the classroom teaching efficiency becomes low, which urgently needs a new learning method to deal with these problems. Focusing on the digital education background of the new curriculum reform, this paper investigates the teaching nature, teaching methods, teaching design and evaluation, classroom construction, and student ability development of music appreciation. In this paper, the Kalman filter algorithm is used, and the optimal transformation order determined in advance is used to suppress the noise in music and improve the sound quality of music appreciation. If the order of the full-band signal model is selected as 10, and the number of sub-bands is selected as 4, then the order of the sub-band signal model should be at least greater than or equal to 2. In the music appreciation teaching test based on the Kalman filter, it was found that 35.6% of the students believed that the music appreciation course based on the Kalman filter could completely improve the learning efficiency, and 49% of students believe that the teaching of music appreciation class based on the Kalman filter can improve a large part of the learning efficiency. Overall, 85.5% of the students believed that the music appreciation class based on the Kalman filter is of great help in improving the learning efficiency, and the Kalman filter denoising method proposed in this paper has an obvious effect, which is a new attempt to promote the digital development of music appreciation.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3765078

DOI: 10.1155/2022/3765078

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